A Comparison of Methods for Generating Bivariate Non-normally Distributed Random Variables

Many distributions of multivariate data in the real world follow a non-normal model with distributions being skewed and/or heavy tailed. In studies in which multivariate non-normal distributions are needed, it is important for simulations of those variables to provide data that is close to the desired parameters while also being fast and easy to perform. Three algorithms for generating multivariate non-normal distributions are reviewed for accuracy, speed and simplicity. They are the Fleishman Power Method, the Fifth-Order Polynomial Transformation Method, and the Generalized Lambda Distribution Method. Simulations were run in order to compare the three methods by how well they generate bivariate distributions with the desired means, variances, skewness, kurtoses, and correlation, simplicity of the algorithms, and how quickly the desired distributions were calculated.

Grantor dc:publisher

UNF Digital Commons

Year dc:date

2009

Author dc:creator

• Stewart, Jaimee E.

• UNF; University of North Florida; Dissertations
• Academic -- UNF -- Mathematics; Dept. of Mathematics and Statistics
• Mathematics
• Statistics and Probability

Repository record dc:identifier

https://digitalcommons.unf.edu/etd/235

OAI identifier oai:identifier

oai:digitalcommons.unf.edu:etd-1236